AI Engineer - W2

AI Engineer

Atlanta, GA

We are seeking a hands-on AI Engineer to design, build, test, deploy, and continuously improve production-grade AI agents, RAG pipelines, and orchestration services for the Quality Engineering AI Center of Excellence. The role will develop AI capabilities supporting requirements review, code quality, test preparation, execution intelligence, defect triage, release assurance, and engineering productivity. Key Responsibilities
AI Agent Development: Develop and deploy AI agents across requirements, code quality, test design, automation, execution, defect analysis, release assurance, and QE intelligence.
RAG and Knowledge Integration: Build grounded RAG pipelines using Jira, Confluence, Git repositories, test assets, defects, incidents, and application documentation.
Agent Orchestration: Implement workflows coordinating multiple AI agents, tools, APIs, and Human-in-the-Loop approvals across SDLC processes.
Enterprise Integration: Develop APIs and microservices and integrate solutions with GitHub/GitLab, Azure DevOps, CI/CD tools, ServiceNow, monitoring platforms, and test-management applications.
Optimization and Evaluation: Improve prompts, chunking, embeddings, retrieval, reranking, model selection, latency, groundedness, accuracy, and token consumption.
Deployment and Support: Containerize and deploy solutions through secure CI/CD pipelines and implement logging, evaluation, observability, feedback, reliability, and production-readiness controls. Required Qualifications
3-8 years of software engineering or AI engineering experience with strong Python programming skills.
Hands-on experience building LLM or Generative AI applications using Agentic AI, RAG, embeddings, vector databases, semantic search, prompt engineering, and model evaluation.
Experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent agent and orchestration frameworks.
Experience developing REST APIs and microservices using FastAPI or a similar framework.
Working knowledge of JavaScript/TypeScript; exposure to Java is an advantage.
Experience with Docker, Kubernetes, Git, GitHub Actions or Azure DevOps pipelines, and cloud deployment.
Knowledge of Azure AI Search, Azure OpenAI, Google Vertex AI, AWS Bedrock, Claude, Gemini, Codex, or equivalent platforms.
Understanding of secure coding, secrets management, access controls, logging, monitoring, and software testing. Preferred Qualifications
Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
Experience in Retail, Supply Chain, Logistics, Distribution, or a related business domain.
Relevant AI, cloud, architecture, software engineering, or Quality Engineering certifications. Key Skills
Python, LLM Applications, Agentic AI, RAG, Embeddings, Vector Databases, Semantic Search, Prompt Engineering, LangChain, LangGraph, FastAPI, REST APIs, TypeScript, Docker, Kubernetes, Git, CI/CD, Azure AI Search, Claude, Gemini, Codex, Jira, Confluence, ServiceNow. Success Measures
Quality and number of AI agents deployed; RAG accuracy, groundedness, retrieval quality, usability, and response time; reduced manual QE effort; improved test coverage and defect detection; stable enterprise integrations; effective control of reliability and LLM/API costs. Ideal Candidate
A hands-on AI engineer who can convert business and Quality Engineering use cases into working prototypes and production-ready solutions. The candidate should be comfortable collaborating with architects, QE engineers, product owners, business SMEs, and distributed delivery teams.